1. Field of the Invention
The present invention generally relates to methods and systems for detecting defects on a specimen by single image detection.
2. Description of the Related Art
The following description and examples are not admitted to be prior art by virtue of their inclusion in this section.
Fabricating semiconductor devices such as logic and memory devices typically includes processing a substrate such as a semiconductor wafer using a large number of semiconductor fabrication processes to form various features and multiple levels of the semiconductor devices. For example, lithography is a semiconductor fabrication process that involves transferring a pattern from a reticle to a resist arranged on a semiconductor wafer. Additional examples of semiconductor fabrication processes include, but are not limited to, chemical-mechanical polishing (CMP), etch, deposition, and ion implantation. Multiple semiconductor devices may be fabricated in an arrangement on a single semiconductor wafer and then separated into individual semiconductor devices.
Inspection processes are used at various steps during a semiconductor manufacturing process to detect defects on wafers to promote higher yield in the manufacturing process and thus higher profits. Inspection has always been an important part of fabricating semiconductor devices such as ICs. However, as the dimensions of semiconductor devices decrease, inspection becomes even more important to the successful manufacture of acceptable semiconductor devices because smaller defects can cause the devices to fail.
There are several currently used methods for detecting defects on specimens such as wafers and reticles. For example, in die to die image comparison, a reference image and a target image are aligned and subtracted. A global threshold is then applied to the subtracted pixels. A binary map corresponding to defect candidates is created. In another embodiment, in cell to cell image comparisons, in the case of periodic structures (e.g., memory devices), the target, image is shifted by one period and compared to itself. More advanced methods can also be used to try to segment the image into different “homogenous” areas and to optimize the thresholding method to the specific segment statistic. This is the principle method behind some detection methods implemented on inspection tools commercially available from KLA-Tencor, Milpitas, Calif., such as segmented auto-thresholding (SAT), median die auto-thresholding (MDAT), HLAT, context-based inspection (CBI), and target based inspection (TBI).
A variation of the above-described methods may be used in some commercially available electron beam inspection tools in the context of scanning electron microscope (SEM) images. The thresholding mechanism is based on the two-dimensional histogram representation (scattergram). In this implementation, the outlier pixels are identified as being outside the principal cloud formed by the joint histograms of the reference and target images. In this case, the pixel value distribution of the target image associated with a given gray value of the reference image is used to estimate the optimized threshold.
There are, however, a number of disadvantages of the above described inspection methods. For example, the die-to-die method requires the acquisition of a minimum of two images (reference and test) which doubles the image acquisition time. Unambiguous detection requires a minimum of three dies to be imaged (two reference and one test). In addition, prior to comparison, test and reference images need to be aligned. Only the overlapped area can be analyzed. Reference images can also introduce some noise in the analysis which always penalizes the signal-to-noise ratio (SNR). A separate module can take care of the defect classification part, but generally leads to poor results due to defect localization issues. Furthermore, any comparison based method will be subject to normal differences between the test and reference patterns. An example of this is line edge roughness (LER). Typical comparison methods limit sensitivity due to nominal LER.
Furthermore, the currently used methods require an algorithm team to design features (“hand-crafted” features) to capture some pertinent information contained in any image. The team needs to address generic questions such as: What is a defect versus noise?; and What can distinguish between different defect classes? The generic assumptions are continuously challenged by read data. To improve the performance for a specific layer/defect type, the team needs to modify the algorithm and new software needs to be released.
Accordingly, it would be advantageous to develop systems and methods for detecting defects on a specimen that do not have one or more of the disadvantages described above.